PEMODELAN PRODUK DOMESTIK BRUTO DI INDONESIA DENGAN PENDEKATAN SEMIPARAMETRIK POLINOMIAL LOKAL DILENGKAPI GUI-R - Muftia Lutfi Cahyani; Suparti Suparti; Budi Warsito (Jurnal Gaussian, 2023)
Gross domestic product is a measuring tool for a country's economy that needs to be known so that the country is able to consider decisions taken regarding future economic policies. The local polynomial semiparametric method that combines parametric regression and local polynomial nonparametric can be one way of predicting a country's GDP. This method is used because in GDP modeling there is one independent variable that has a linear relationship while the other variables have a pattern that tends to cluster. The modeling aims to obtain a semiparametric local polynomial model on GDP in Indonesia with the influence of coal export volume as a parametric independent variable and world oil prices as a nonparametric independent variable from the first quarter of 2005 to the second quarter of 2021 which is equipped with a GUI to simplify calculations. Based on experiments on several types of kernels, bandwidth and model degrees, the best model is local polynomial semiparametric model with Gaussian kernel weighting at degree 2 which has the smallest GCV. This model also has an R-Square value of 89.2% where the value of GDP is strongly influenced by world oil prices and coal export volumes together. The forecasting ability of this best model is said to be good because it has a MAPE of 17.127%.